# Distributed Detection via Bayesian Updates and Consensus

arXiv: https://arxiv.org/abs/1412.6049 · HTML: https://mlsanity.com/paper/1412.6049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2014.

## Built on by (most cited first, 1 of 1)

- [The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains](https://mlsanity.com/paper/2403.18343/) (2024)

## Abstract (arXiv)

In this paper, we discuss a class of distributed detection algorithms which can be viewed as implementations of Bayes' law in distributed settings. Some of the algorithms are proposed in the literature most recently, and others are first developed in this paper. The common feature of these algorithms is that they all combine (i) certain kinds of consensus protocols with (ii) Bayesian updates. They are different mainly in the aspect of the type of consensus protocol and the order of the two operations. After discussing their similarities and differences, we compare these distributed algorithms by numerical examples. We focus on the rate at which these algorithms detect the underlying true state of an object. We find that (a) The algorithms with consensus via geometric average is more efficient than that via arithmetic average; (b) The order of consensus aggregation and Bayesian update does not apparently influence the performance of the algorithms; (c) The existence of communication delay dramatically slows down the rate of convergence; (d) More communication between agents with different signal structures improves the rate of convergence.